Terum
Terum is an applied AI firm that embeds engineers into client teams and provides a five-part platform—Observe, Capture, Relay, Construct, and Train—to measure AI impact against a baseline and roll out effective workflows across the organization. The company works with AI tools like Claude Code, Codex, and Cursor, and leaves the platform and trained teams in place after each engagement.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Engineering organizations struggle to capture and distribute the tacit knowledge behind their best AI agents. High-performing engineers often develop effective custom skills and workflows that remain siloed, leaving less experienced team members to rely on generic model behavior that is slower, costlier, and prone to errors.
Solution
Terum provides a framework for evaluating, publishing, and monitoring AI agent skills so teams can systematically share what works. The platform runs head-to-head A/B tests—candidate skill versus baseline—in fresh sandboxes on the user's own machine, generating deterministic pass/fail verdicts backed by cost, time, and quality comparisons. It creates personalized test cases that emulate the team's production environment and produces auditable receipts for every evaluation run. Governance controls, security scanning, and multi-version comparison allow teams to confidently distribute skills at scale, while continuous monitoring flags when model updates degrade a skill's effectiveness. The tool is designed to be used directly within engineering workflows, with results stored locally and shared via published versions.
Target Audience
Primary users include engineering leaders and platform teams at software companies deploying AI coding agents who want to raise baseline performance across all engineers. It also serves teams with formal governance needs around skill review, security, and audit compliance.
Features
- A/B test framework that runs the same tasks with and without a skill in isolated sandboxes, producing net-lift verdicts (PASS/NEUTRAL/FAIL) based on deterministic checks and judge-based transcript comparisons
- Automated test case generation that creates personalized eval cases from the team's production workflow when no authored evals exist
- Audit receipts recording every publish, install, eval, and approval with cryptographic digests of the exact bytes evaluated
- Security scanning of every skill version for risky tool grants, shell commands, network calls, and secrets before installation
- Governance controls permitting admins to set review requirements, tool access limits, and eval pass thresholds for publishing
- Continuous degradation monitoring that alerts teams when model updates impact skill performance, with three-arm comparison (baseline, candidate, incumbent) for teams
- Trigger evaluation that checks whether the skill would be selected by the model given the Library's skill catalog
- Sign test and cost-per-run reporting (turns, time, dollars) so wins are shown alongside their expense implications